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\begin{document}

\title{Data-driven sampling pattern design for sparse spotlight SAR imaging}



\author[1]{Yao Zhao}%
\author[1]{Wenkun Huang}%
\author[2]{Xiangyin Quan}%
\author[1]{Bingo Ling}%
\author[3]{Zhe Zhang}%
\affil[1]{Guangdong University of Technology}%
\affil[2]{China Academy of Launch Vehicle Technology}%
\affil[3]{Aerospace Information Research Institute, Chinese Academy of Sciences}%


\vspace{-1em}



  
  \date{June 16, 2024}


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\begin{abstract}
This paper proposes a joint optimization method for the imaging
algorithm and sampling scheme of sparse spotlight syhthetic aperture
radar (SAR) imaging based on deep convolutional neural networks.
Traditional compressed sensing (CS) based sparse SAR imaging has been
widely studied. Deep learning and sparse unfolding networks have been
introduced into sparse SAR imaging, but most current works focus only on
the imaging stage and simply adopt the conventional uniform or random
down-sampling scheme. Considering that the imaging quality also depends
on the sampling pattern besides the imaging algorithm, this paper
introduces a learning-based strategy to jointly optimize the sampling
scheme and the imaging network parameters of the reconstruction module.
In a deep learning-based image reconstruction scheme, joint and
continuous optimization of the sampling patterns and convolutional
neural network parameters is achieved to improve the image quality.
Simulation results based on real SAR image dataset illustrate the
effectiveness and superiority of the proposed framework.%
\end{abstract}%



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